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Macros</h2></td></tr>
<tr class="memitem:a710b6e009261290c6151f329cf409530"><td class="memItemLeft" align="right" valign="top">#define&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="arm__nnfunctions_8h.html#a710b6e009261290c6151f329cf409530">USE_INTRINSIC</a></td></tr>
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Enumerations</h2></td></tr>
<tr class="memitem:a7f41aa78cd9a0552fae9b348ee4831a0"><td class="memItemLeft" align="right" valign="top">enum &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="arm__nnfunctions_8h.html#a7f41aa78cd9a0552fae9b348ee4831a0">arm_nn_activation_type</a> </td></tr>
<tr class="memdesc:a7f41aa78cd9a0552fae9b348ee4831a0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Struct for specifying activation function types.  <a href="arm__nnfunctions_8h.html#a7f41aa78cd9a0552fae9b348ee4831a0">More...</a><br/></td></tr>
<tr class="separator:a7f41aa78cd9a0552fae9b348ee4831a0"><td class="memSeparator" colspan="2">&#160;</td></tr>
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Functions</h2></td></tr>
<tr class="memitem:ga5ac772a94937e1efdab145a2ab4c0927"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga5ac772a94937e1efdab145a2ab4c0927">arm_convolve_wrapper_s8</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__conv__params.html">cmsis_nn_conv_params</a> *conv_params, const <a class="el" href="structcmsis__nn__per__channel__quant__params.html">cmsis_nn_per_channel_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int32_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:ga5ac772a94937e1efdab145a2ab4c0927"><td class="mdescLeft">&#160;</td><td class="mdescRight">s8 convolution layer wrapper function with the main purpose to call the optimal kernel available in cmsis-nn to perform the convolution.  <a href="group__NNConv.html#ga5ac772a94937e1efdab145a2ab4c0927">More...</a><br/></td></tr>
<tr class="separator:ga5ac772a94937e1efdab145a2ab4c0927"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga60c117b646f95abeb9966eefc4a9da38"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga60c117b646f95abeb9966eefc4a9da38">arm_convolve_wrapper_s8_get_buffer_size</a> (const <a class="el" href="structcmsis__nn__conv__params.html">cmsis_nn_conv_params</a> *conv_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims)</td></tr>
<tr class="memdesc:ga60c117b646f95abeb9966eefc4a9da38"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the required buffer size for arm_convolve_wrapper_s8.  <a href="group__NNConv.html#ga60c117b646f95abeb9966eefc4a9da38">More...</a><br/></td></tr>
<tr class="separator:ga60c117b646f95abeb9966eefc4a9da38"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gaf1b719fadc837ba10c065fa0aa1d31fc"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gaf1b719fadc837ba10c065fa0aa1d31fc">arm_convolve_wrapper_s16</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__conv__params.html">cmsis_nn_conv_params</a> *conv_params, const <a class="el" href="structcmsis__nn__per__channel__quant__params.html">cmsis_nn_per_channel_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q15_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int64_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q15_t *output_data)</td></tr>
<tr class="memdesc:gaf1b719fadc837ba10c065fa0aa1d31fc"><td class="mdescLeft">&#160;</td><td class="mdescRight">s16 convolution layer wrapper function with the main purpose to call the optimal kernel available in cmsis-nn to perform the convolution.  <a href="group__NNConv.html#gaf1b719fadc837ba10c065fa0aa1d31fc">More...</a><br/></td></tr>
<tr class="separator:gaf1b719fadc837ba10c065fa0aa1d31fc"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga818dfca6a26af7cc8abb91d43fb16930"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga818dfca6a26af7cc8abb91d43fb16930">arm_convolve_wrapper_s16_get_buffer_size</a> (const <a class="el" href="structcmsis__nn__conv__params.html">cmsis_nn_conv_params</a> *conv_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims)</td></tr>
<tr class="memdesc:ga818dfca6a26af7cc8abb91d43fb16930"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the required buffer size for arm_convolve_wrapper_s16.  <a href="group__NNConv.html#ga818dfca6a26af7cc8abb91d43fb16930">More...</a><br/></td></tr>
<tr class="separator:ga818dfca6a26af7cc8abb91d43fb16930"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga34bb5c05805ca7d5ea9681fcad8a711d"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga34bb5c05805ca7d5ea9681fcad8a711d">arm_convolve_s8</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__conv__params.html">cmsis_nn_conv_params</a> *conv_params, const <a class="el" href="structcmsis__nn__per__channel__quant__params.html">cmsis_nn_per_channel_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int32_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:ga34bb5c05805ca7d5ea9681fcad8a711d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Basic s8 convolution function.  <a href="group__NNConv.html#ga34bb5c05805ca7d5ea9681fcad8a711d">More...</a><br/></td></tr>
<tr class="separator:ga34bb5c05805ca7d5ea9681fcad8a711d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gac057b0749e2a6828ae21f762f3fa0c85"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gac057b0749e2a6828ae21f762f3fa0c85">arm_convolve_s8_get_buffer_size</a> (const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims)</td></tr>
<tr class="memdesc:gac057b0749e2a6828ae21f762f3fa0c85"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the required buffer size for s8 convolution function.  <a href="group__NNConv.html#gac057b0749e2a6828ae21f762f3fa0c85">More...</a><br/></td></tr>
<tr class="separator:gac057b0749e2a6828ae21f762f3fa0c85"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gaf3e58798b12fa230d6a6487887aa13bf"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gaf3e58798b12fa230d6a6487887aa13bf">arm_convolve_s16</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__conv__params.html">cmsis_nn_conv_params</a> *conv_params, const <a class="el" href="structcmsis__nn__per__channel__quant__params.html">cmsis_nn_per_channel_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q15_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int64_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q15_t *output_data)</td></tr>
<tr class="memdesc:gaf3e58798b12fa230d6a6487887aa13bf"><td class="mdescLeft">&#160;</td><td class="mdescRight">Basic s16 convolution function.  <a href="group__NNConv.html#gaf3e58798b12fa230d6a6487887aa13bf">More...</a><br/></td></tr>
<tr class="separator:gaf3e58798b12fa230d6a6487887aa13bf"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gadda1e5325d511d56716111548c787a1c"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gadda1e5325d511d56716111548c787a1c">arm_convolve_fast_s16</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__conv__params.html">cmsis_nn_conv_params</a> *conv_params, const <a class="el" href="structcmsis__nn__per__channel__quant__params.html">cmsis_nn_per_channel_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q15_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int64_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q15_t *output_data)</td></tr>
<tr class="memdesc:gadda1e5325d511d56716111548c787a1c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Optimized s16 convolution function.  <a href="group__NNConv.html#gadda1e5325d511d56716111548c787a1c">More...</a><br/></td></tr>
<tr class="separator:gadda1e5325d511d56716111548c787a1c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gaf8ee642967e4b1b05465621979de1baa"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gaf8ee642967e4b1b05465621979de1baa">arm_convolve_s16_get_buffer_size</a> (const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims)</td></tr>
<tr class="memdesc:gaf8ee642967e4b1b05465621979de1baa"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the required buffer size for s16 convolution function.  <a href="group__NNConv.html#gaf8ee642967e4b1b05465621979de1baa">More...</a><br/></td></tr>
<tr class="separator:gaf8ee642967e4b1b05465621979de1baa"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga8db7c4be84de273cfba630106d912737"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga8db7c4be84de273cfba630106d912737">arm_convolve_fast_s16_get_buffer_size</a> (const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims)</td></tr>
<tr class="memdesc:ga8db7c4be84de273cfba630106d912737"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the required buffer size for fast s16 convolution function.  <a href="group__NNConv.html#ga8db7c4be84de273cfba630106d912737">More...</a><br/></td></tr>
<tr class="separator:ga8db7c4be84de273cfba630106d912737"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga210ae8d8fc1d12ee15b41f1fa6947681"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga210ae8d8fc1d12ee15b41f1fa6947681">arm_convolve_HWC_q7_basic</a> (const q7_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out, q15_t *bufferA, q7_t *bufferB)</td></tr>
<tr class="memdesc:ga210ae8d8fc1d12ee15b41f1fa6947681"><td class="mdescLeft">&#160;</td><td class="mdescRight">Basic Q7 convolution function.  <a href="group__NNConv.html#ga210ae8d8fc1d12ee15b41f1fa6947681">More...</a><br/></td></tr>
<tr class="separator:ga210ae8d8fc1d12ee15b41f1fa6947681"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga4501fa22c0836002aa47ccc313dce252"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga4501fa22c0836002aa47ccc313dce252">arm_convolve_HWC_q7_basic_nonsquare</a> (const q7_t *Im_in, const uint16_t dim_im_in_x, const uint16_t dim_im_in_y, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel_x, const uint16_t dim_kernel_y, const uint16_t padding_x, const uint16_t padding_y, const uint16_t stride_x, const uint16_t stride_y, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out_x, const uint16_t dim_im_out_y, q15_t *bufferA, q7_t *bufferB)</td></tr>
<tr class="memdesc:ga4501fa22c0836002aa47ccc313dce252"><td class="mdescLeft">&#160;</td><td class="mdescRight">Basic Q7 convolution function (non-square shape)  <a href="group__NNConv.html#ga4501fa22c0836002aa47ccc313dce252">More...</a><br/></td></tr>
<tr class="separator:ga4501fa22c0836002aa47ccc313dce252"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga55701f213b198084b52eab53097f1f58"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga55701f213b198084b52eab53097f1f58">arm_convolve_HWC_q15_basic</a> (const q15_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const q15_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const q15_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q15_t *Im_out, const uint16_t dim_im_out, q15_t *bufferA, q7_t *bufferB)</td></tr>
<tr class="memdesc:ga55701f213b198084b52eab53097f1f58"><td class="mdescLeft">&#160;</td><td class="mdescRight">Basic Q15 convolution function.  <a href="group__NNConv.html#ga55701f213b198084b52eab53097f1f58">More...</a><br/></td></tr>
<tr class="separator:ga55701f213b198084b52eab53097f1f58"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gae00d3c1285907d59657369fc98bcc83f"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gae00d3c1285907d59657369fc98bcc83f">arm_convolve_HWC_q7_fast</a> (const q7_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out, q15_t *bufferA, q7_t *bufferB)</td></tr>
<tr class="memdesc:gae00d3c1285907d59657369fc98bcc83f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fast Q7 convolution function.  <a href="group__NNConv.html#gae00d3c1285907d59657369fc98bcc83f">More...</a><br/></td></tr>
<tr class="separator:gae00d3c1285907d59657369fc98bcc83f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gabc6d6b991024e9e5c5cdbd7489de88ef"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gabc6d6b991024e9e5c5cdbd7489de88ef">arm_convolve_HWC_q7_fast_nonsquare</a> (const q7_t *Im_in, const uint16_t dim_im_in_x, const uint16_t dim_im_in_y, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel_x, const uint16_t dim_kernel_y, const uint16_t padding_x, const uint16_t padding_y, const uint16_t stride_x, const uint16_t stride_y, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out_x, const uint16_t dim_im_out_y, q15_t *bufferA, q7_t *bufferB)</td></tr>
<tr class="memdesc:gabc6d6b991024e9e5c5cdbd7489de88ef"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fast Q7 convolution function (non-sqaure shape)  <a href="group__NNConv.html#gabc6d6b991024e9e5c5cdbd7489de88ef">More...</a><br/></td></tr>
<tr class="separator:gabc6d6b991024e9e5c5cdbd7489de88ef"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga110adcfdaab356c750c6270aa5e05f29"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga110adcfdaab356c750c6270aa5e05f29">arm_convolve_1x1_HWC_q7_fast_nonsquare</a> (const q7_t *Im_in, const uint16_t dim_im_in_x, const uint16_t dim_im_in_y, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel_x, const uint16_t dim_kernel_y, const uint16_t padding_x, const uint16_t padding_y, const uint16_t stride_x, const uint16_t stride_y, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out_x, const uint16_t dim_im_out_y, q15_t *bufferA, q7_t *bufferB)</td></tr>
<tr class="memdesc:ga110adcfdaab356c750c6270aa5e05f29"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fast Q7 version of 1x1 convolution (non-sqaure shape)  <a href="group__NNConv.html#ga110adcfdaab356c750c6270aa5e05f29">More...</a><br/></td></tr>
<tr class="separator:ga110adcfdaab356c750c6270aa5e05f29"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga53608c814d9b8a458f4d87f8f40051b7"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga53608c814d9b8a458f4d87f8f40051b7">arm_convolve_1x1_s8_fast</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__conv__params.html">cmsis_nn_conv_params</a> *conv_params, const <a class="el" href="structcmsis__nn__per__channel__quant__params.html">cmsis_nn_per_channel_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int32_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:ga53608c814d9b8a458f4d87f8f40051b7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fast s8 version for 1x1 convolution (non-square shape)  <a href="group__NNConv.html#ga53608c814d9b8a458f4d87f8f40051b7">More...</a><br/></td></tr>
<tr class="separator:ga53608c814d9b8a458f4d87f8f40051b7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gaf8bf3e2c2f8b4f2a0cbfb8a797a8b5a8"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gaf8bf3e2c2f8b4f2a0cbfb8a797a8b5a8">arm_convolve_1x1_s8_fast_get_buffer_size</a> (const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims)</td></tr>
<tr class="memdesc:gaf8bf3e2c2f8b4f2a0cbfb8a797a8b5a8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the required buffer size for arm_convolve_1x1_s8_fast.  <a href="group__NNConv.html#gaf8bf3e2c2f8b4f2a0cbfb8a797a8b5a8">More...</a><br/></td></tr>
<tr class="separator:gaf8bf3e2c2f8b4f2a0cbfb8a797a8b5a8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga5b85b56e06e563c05b08ad6ba7423e1d"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga5b85b56e06e563c05b08ad6ba7423e1d">arm_convolve_1_x_n_s8</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__conv__params.html">cmsis_nn_conv_params</a> *conv_params, const <a class="el" href="structcmsis__nn__per__channel__quant__params.html">cmsis_nn_per_channel_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int32_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:ga5b85b56e06e563c05b08ad6ba7423e1d"><td class="mdescLeft">&#160;</td><td class="mdescRight">1xn convolution  <a href="group__NNConv.html#ga5b85b56e06e563c05b08ad6ba7423e1d">More...</a><br/></td></tr>
<tr class="separator:ga5b85b56e06e563c05b08ad6ba7423e1d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gaf87f133e81ee6c1fbde89fe44c35352e"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gaf87f133e81ee6c1fbde89fe44c35352e">arm_convolve_1_x_n_s8_get_buffer_size</a> (const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims)</td></tr>
<tr class="memdesc:gaf87f133e81ee6c1fbde89fe44c35352e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the required additional buffer size for 1xn convolution.  <a href="group__NNConv.html#gaf87f133e81ee6c1fbde89fe44c35352e">More...</a><br/></td></tr>
<tr class="separator:gaf87f133e81ee6c1fbde89fe44c35352e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga98f2ead67d7cbdf558b0cd8a3b8fc148"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga98f2ead67d7cbdf558b0cd8a3b8fc148">arm_convolve_HWC_q7_RGB</a> (const q7_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out, q15_t *bufferA, q7_t *bufferB)</td></tr>
<tr class="memdesc:ga98f2ead67d7cbdf558b0cd8a3b8fc148"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q7 version of convolution for RGB image.  <a href="group__NNConv.html#ga98f2ead67d7cbdf558b0cd8a3b8fc148">More...</a><br/></td></tr>
<tr class="separator:ga98f2ead67d7cbdf558b0cd8a3b8fc148"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga4efb1ccbbaa7dd936961989dcb443f50"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga4efb1ccbbaa7dd936961989dcb443f50">arm_convolve_HWC_q15_fast</a> (const q15_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const q15_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const q15_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q15_t *Im_out, const uint16_t dim_im_out, q15_t *bufferA, q7_t *bufferB)</td></tr>
<tr class="memdesc:ga4efb1ccbbaa7dd936961989dcb443f50"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fast Q15 convolution function.  <a href="group__NNConv.html#ga4efb1ccbbaa7dd936961989dcb443f50">More...</a><br/></td></tr>
<tr class="separator:ga4efb1ccbbaa7dd936961989dcb443f50"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga614ec3b71eb96e29952ec3f09e7b9c3c"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga614ec3b71eb96e29952ec3f09e7b9c3c">arm_convolve_HWC_q15_fast_nonsquare</a> (const q15_t *Im_in, const uint16_t dim_im_in_x, const uint16_t dim_im_in_y, const uint16_t ch_im_in, const q15_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel_x, const uint16_t dim_kernel_y, const uint16_t padding_x, const uint16_t padding_y, const uint16_t stride_x, const uint16_t stride_y, const q15_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q15_t *Im_out, const uint16_t dim_im_out_x, const uint16_t dim_im_out_y, q15_t *bufferA, q7_t *bufferB)</td></tr>
<tr class="memdesc:ga614ec3b71eb96e29952ec3f09e7b9c3c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fast Q15 convolution function (non-sqaure shape)  <a href="group__NNConv.html#ga614ec3b71eb96e29952ec3f09e7b9c3c">More...</a><br/></td></tr>
<tr class="separator:ga614ec3b71eb96e29952ec3f09e7b9c3c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gad3d21b3bc6dbd6f3b97d01104349cb0a"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gad3d21b3bc6dbd6f3b97d01104349cb0a">arm_depthwise_separable_conv_HWC_q7</a> (const q7_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out, q15_t *bufferA, q7_t *bufferB)</td></tr>
<tr class="memdesc:gad3d21b3bc6dbd6f3b97d01104349cb0a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q7 depthwise separable convolution function.  <a href="group__NNConv.html#gad3d21b3bc6dbd6f3b97d01104349cb0a">More...</a><br/></td></tr>
<tr class="separator:gad3d21b3bc6dbd6f3b97d01104349cb0a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga32ac508c5467813a84f74f96655dc697"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga32ac508c5467813a84f74f96655dc697">arm_depthwise_separable_conv_HWC_q7_nonsquare</a> (const q7_t *Im_in, const uint16_t dim_im_in_x, const uint16_t dim_im_in_y, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel_x, const uint16_t dim_kernel_y, const uint16_t padding_x, const uint16_t padding_y, const uint16_t stride_x, const uint16_t stride_y, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out_x, const uint16_t dim_im_out_y, q15_t *bufferA, q7_t *bufferB)</td></tr>
<tr class="memdesc:ga32ac508c5467813a84f74f96655dc697"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q7 depthwise separable convolution function (non-square shape)  <a href="group__NNConv.html#ga32ac508c5467813a84f74f96655dc697">More...</a><br/></td></tr>
<tr class="separator:ga32ac508c5467813a84f74f96655dc697"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gabca128df92f9486ebdac7eee3178c0fd"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gabca128df92f9486ebdac7eee3178c0fd">arm_depthwise_conv_wrapper_s8</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__dw__conv__params.html">cmsis_nn_dw_conv_params</a> *dw_conv_params, const <a class="el" href="structcmsis__nn__per__channel__quant__params.html">cmsis_nn_per_channel_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int32_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:gabca128df92f9486ebdac7eee3178c0fd"><td class="mdescLeft">&#160;</td><td class="mdescRight">Wrapper function to pick the right optimized s8 depthwise convolution function.  <a href="group__NNConv.html#gabca128df92f9486ebdac7eee3178c0fd">More...</a><br/></td></tr>
<tr class="separator:gabca128df92f9486ebdac7eee3178c0fd"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga100b66e41b8fdf1eeb0c108bca6b6deb"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga100b66e41b8fdf1eeb0c108bca6b6deb">arm_depthwise_conv_wrapper_s8_get_buffer_size</a> (const <a class="el" href="structcmsis__nn__dw__conv__params.html">cmsis_nn_dw_conv_params</a> *dw_conv_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims)</td></tr>
<tr class="memdesc:ga100b66e41b8fdf1eeb0c108bca6b6deb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get size of additional buffer required by <a class="el" href="group__NNConv.html#gabca128df92f9486ebdac7eee3178c0fd" title="Wrapper function to pick the right optimized s8 depthwise convolution function. ">arm_depthwise_conv_wrapper_s8()</a>  <a href="group__NNConv.html#ga100b66e41b8fdf1eeb0c108bca6b6deb">More...</a><br/></td></tr>
<tr class="separator:ga100b66e41b8fdf1eeb0c108bca6b6deb"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gad8a6832cf0c8a8862fc8cd9ec95f40a9"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gad8a6832cf0c8a8862fc8cd9ec95f40a9">arm_depthwise_conv_s8</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__dw__conv__params.html">cmsis_nn_dw_conv_params</a> *dw_conv_params, const <a class="el" href="structcmsis__nn__per__channel__quant__params.html">cmsis_nn_per_channel_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int32_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:gad8a6832cf0c8a8862fc8cd9ec95f40a9"><td class="mdescLeft">&#160;</td><td class="mdescRight">Basic s8 depthwise convolution function that doesn't have any constraints on the input dimensions.  <a href="group__NNConv.html#gad8a6832cf0c8a8862fc8cd9ec95f40a9">More...</a><br/></td></tr>
<tr class="separator:gad8a6832cf0c8a8862fc8cd9ec95f40a9"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga3e5c3370f6f70ac51d559a53290bb45d"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga3e5c3370f6f70ac51d559a53290bb45d">arm_depthwise_conv_s16</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__dw__conv__params.html">cmsis_nn_dw_conv_params</a> *dw_conv_params, const <a class="el" href="structcmsis__nn__per__channel__quant__params.html">cmsis_nn_per_channel_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q15_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int64_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q15_t *output_data)</td></tr>
<tr class="memdesc:ga3e5c3370f6f70ac51d559a53290bb45d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Basic s16 depthwise convolution function that doesn't have any constraints on the input dimensions.  <a href="group__NNConv.html#ga3e5c3370f6f70ac51d559a53290bb45d">More...</a><br/></td></tr>
<tr class="separator:ga3e5c3370f6f70ac51d559a53290bb45d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga558337f134d84dac2c312c5175bd67de"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga558337f134d84dac2c312c5175bd67de">arm_depthwise_conv_3x3_s8</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__dw__conv__params.html">cmsis_nn_dw_conv_params</a> *dw_conv_params, const <a class="el" href="structcmsis__nn__per__channel__quant__params.html">cmsis_nn_per_channel_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int32_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:ga558337f134d84dac2c312c5175bd67de"><td class="mdescLeft">&#160;</td><td class="mdescRight">Optimized s8 depthwise convolution function for 3x3 kernel size with some constraints on the input arguments(documented below). Refer <a class="el" href="group__NNConv.html#gad8a6832cf0c8a8862fc8cd9ec95f40a9" title="Basic s8 depthwise convolution function that doesn&#39;t have any constraints on the input dimensions...">arm_depthwise_conv_s8()</a> for function argument details.  <a href="group__NNConv.html#ga558337f134d84dac2c312c5175bd67de">More...</a><br/></td></tr>
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<tr class="memitem:gab9dc832c407c0fe0f4d6fbb063e85e97"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#gab9dc832c407c0fe0f4d6fbb063e85e97">arm_depthwise_conv_s8_opt</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__dw__conv__params.html">cmsis_nn_dw_conv_params</a> *dw_conv_params, const <a class="el" href="structcmsis__nn__per__channel__quant__params.html">cmsis_nn_per_channel_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int32_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:gab9dc832c407c0fe0f4d6fbb063e85e97"><td class="mdescLeft">&#160;</td><td class="mdescRight">Optimized s8 depthwise convolution function with constraint that in_channel equals out_channel. Refer <a class="el" href="group__NNConv.html#gad8a6832cf0c8a8862fc8cd9ec95f40a9" title="Basic s8 depthwise convolution function that doesn&#39;t have any constraints on the input dimensions...">arm_depthwise_conv_s8()</a> for function argument details.  <a href="group__NNConv.html#gab9dc832c407c0fe0f4d6fbb063e85e97">More...</a><br/></td></tr>
<tr class="separator:gab9dc832c407c0fe0f4d6fbb063e85e97"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga0ee6e4c1a521657c35477ae0daf1c842"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga0ee6e4c1a521657c35477ae0daf1c842">arm_depthwise_conv_s8_opt_get_buffer_size</a> (const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims)</td></tr>
<tr class="memdesc:ga0ee6e4c1a521657c35477ae0daf1c842"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the required buffer size for optimized s8 depthwise convolution function with constraint that in_channel equals out_channel.  <a href="group__NNConv.html#ga0ee6e4c1a521657c35477ae0daf1c842">More...</a><br/></td></tr>
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<tr class="memitem:ga8b7e0c2e989e8c75f0dc789f3115323d"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__FC.html#ga8b7e0c2e989e8c75f0dc789f3115323d">arm_fully_connected_q7</a> (const q7_t *pV, const q7_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q7_t *pOut, q15_t *vec_buffer)</td></tr>
<tr class="memdesc:ga8b7e0c2e989e8c75f0dc789f3115323d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q7 basic fully-connected layer function.  <a href="group__FC.html#ga8b7e0c2e989e8c75f0dc789f3115323d">More...</a><br/></td></tr>
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<tr class="memitem:ga30d44fed122f2e159a417f9c12181ded"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__FC.html#ga30d44fed122f2e159a417f9c12181ded">arm_fully_connected_s8</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__fc__params.html">cmsis_nn_fc_params</a> *fc_params, const <a class="el" href="structcmsis__nn__per__tensor__quant__params.html">cmsis_nn_per_tensor_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int32_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:ga30d44fed122f2e159a417f9c12181ded"><td class="mdescLeft">&#160;</td><td class="mdescRight">Basic s8 Fully Connected function.  <a href="group__FC.html#ga30d44fed122f2e159a417f9c12181ded">More...</a><br/></td></tr>
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<tr class="memitem:ga88e541ff9fb088ebccb79a7a6b8bbe1e"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__FC.html#ga88e541ff9fb088ebccb79a7a6b8bbe1e">arm_fully_connected_s8_get_buffer_size</a> (const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims)</td></tr>
<tr class="memdesc:ga88e541ff9fb088ebccb79a7a6b8bbe1e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the required buffer size for S8 basic fully-connected and matrix multiplication layer function for TF Lite.  <a href="group__FC.html#ga88e541ff9fb088ebccb79a7a6b8bbe1e">More...</a><br/></td></tr>
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<tr class="memitem:ga22aa22fe80e323429e8ac6aaeef878e8"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__FC.html#ga22aa22fe80e323429e8ac6aaeef878e8">arm_fully_connected_s16</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__fc__params.html">cmsis_nn_fc_params</a> *fc_params, const <a class="el" href="structcmsis__nn__per__tensor__quant__params.html">cmsis_nn_per_tensor_quant_params</a> *quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q15_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const q7_t *filter_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const int64_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q15_t *output_data)</td></tr>
<tr class="memdesc:ga22aa22fe80e323429e8ac6aaeef878e8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Basic s16 Fully Connected function.  <a href="group__FC.html#ga22aa22fe80e323429e8ac6aaeef878e8">More...</a><br/></td></tr>
<tr class="separator:ga22aa22fe80e323429e8ac6aaeef878e8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga5f0e89482a3ea7ab417630be80ca983d"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__FC.html#ga5f0e89482a3ea7ab417630be80ca983d">arm_fully_connected_s16_get_buffer_size</a> (const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims)</td></tr>
<tr class="memdesc:ga5f0e89482a3ea7ab417630be80ca983d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the required buffer size for S16 basic fully-connected and matrix multiplication layer function for TF Lite.  <a href="group__FC.html#ga5f0e89482a3ea7ab417630be80ca983d">More...</a><br/></td></tr>
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<tr class="memitem:gaf82b71ef472a38f8fc9ac414d9d07e67"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__FC.html#gaf82b71ef472a38f8fc9ac414d9d07e67">arm_fully_connected_q7_opt</a> (const q7_t *pV, const q7_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q7_t *pOut, q15_t *vec_buffer)</td></tr>
<tr class="memdesc:gaf82b71ef472a38f8fc9ac414d9d07e67"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q7 opt fully-connected layer function.  <a href="group__FC.html#gaf82b71ef472a38f8fc9ac414d9d07e67">More...</a><br/></td></tr>
<tr class="separator:gaf82b71ef472a38f8fc9ac414d9d07e67"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gaac666c212b209e636c2369dd5c75d0dc"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__FC.html#gaac666c212b209e636c2369dd5c75d0dc">arm_fully_connected_q15</a> (const q15_t *pV, const q15_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q15_t *bias, q15_t *pOut, q15_t *vec_buffer)</td></tr>
<tr class="memdesc:gaac666c212b209e636c2369dd5c75d0dc"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q15 basic fully-connected layer function.  <a href="group__FC.html#gaac666c212b209e636c2369dd5c75d0dc">More...</a><br/></td></tr>
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<tr class="memitem:ga062912078da113f5dd2004fd919a0ff2"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__FC.html#ga062912078da113f5dd2004fd919a0ff2">arm_fully_connected_q15_opt</a> (const q15_t *pV, const q15_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q15_t *bias, q15_t *pOut, q15_t *vec_buffer)</td></tr>
<tr class="memdesc:ga062912078da113f5dd2004fd919a0ff2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q15 opt fully-connected layer function.  <a href="group__FC.html#ga062912078da113f5dd2004fd919a0ff2">More...</a><br/></td></tr>
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<tr class="memitem:ga4a1521e7532a1e62d71f3b12762016e2"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__FC.html#ga4a1521e7532a1e62d71f3b12762016e2">arm_fully_connected_mat_q7_vec_q15</a> (const q15_t *pV, const q7_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q15_t *pOut, q15_t *vec_buffer)</td></tr>
<tr class="memdesc:ga4a1521e7532a1e62d71f3b12762016e2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Mixed Q15-Q7 fully-connected layer function.  <a href="group__FC.html#ga4a1521e7532a1e62d71f3b12762016e2">More...</a><br/></td></tr>
<tr class="separator:ga4a1521e7532a1e62d71f3b12762016e2"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gae3857bb6375692e81dde8cbd70adec08"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__FC.html#gae3857bb6375692e81dde8cbd70adec08">arm_fully_connected_mat_q7_vec_q15_opt</a> (const q15_t *pV, const q7_t *pM, const uint16_t dim_vec, const uint16_t num_of_rows, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q15_t *pOut, q15_t *vec_buffer)</td></tr>
<tr class="memdesc:gae3857bb6375692e81dde8cbd70adec08"><td class="mdescLeft">&#160;</td><td class="mdescRight">Mixed Q15-Q7 opt fully-connected layer function.  <a href="group__FC.html#gae3857bb6375692e81dde8cbd70adec08">More...</a><br/></td></tr>
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<tr class="memitem:abc4fb258cfe8500ee68e812a293a80a3"><td class="memItemLeft" align="right" valign="top">q7_t *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="arm__nnfunctions_8h.html#abc4fb258cfe8500ee68e812a293a80a3">arm_nn_mat_mult_kernel_q7_q15</a> (const q7_t *pA, const q15_t *pInBuffer, const uint16_t ch_im_out, const uint16_t numCol_A, const uint16_t bias_shift, const uint16_t out_shift, const q7_t *bias, q7_t *pOut)</td></tr>
<tr class="memdesc:abc4fb258cfe8500ee68e812a293a80a3"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix-Multiplication Kernels for Convolution.  <a href="#abc4fb258cfe8500ee68e812a293a80a3">More...</a><br/></td></tr>
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<tr class="memitem:ga19f5c245db9ecfb7918fe1fc4f16af2b"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__BasicMath.html#ga19f5c245db9ecfb7918fe1fc4f16af2b">arm_elementwise_add_s8</a> (const int8_t *input_1_vect, const int8_t *input_2_vect, const int32_t input_1_offset, const int32_t input_1_mult, const int32_t input_1_shift, const int32_t input_2_offset, const int32_t input_2_mult, const int32_t input_2_shift, const int32_t left_shift, int8_t *output, const int32_t out_offset, const int32_t out_mult, const int32_t out_shift, const int32_t out_activation_min, const int32_t out_activation_max, const int32_t block_size)</td></tr>
<tr class="memdesc:ga19f5c245db9ecfb7918fe1fc4f16af2b"><td class="mdescLeft">&#160;</td><td class="mdescRight">s8 elementwise add of two vectors  <a href="group__BasicMath.html#ga19f5c245db9ecfb7918fe1fc4f16af2b">More...</a><br/></td></tr>
<tr class="separator:ga19f5c245db9ecfb7918fe1fc4f16af2b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga9e988362b8860a0108e62caf03e19b45"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__BasicMath.html#ga9e988362b8860a0108e62caf03e19b45">arm_elementwise_add_s16</a> (const int16_t *input_1_vect, const int16_t *input_2_vect, const int32_t input_1_offset, const int32_t input_1_mult, const int32_t input_1_shift, const int32_t input_2_offset, const int32_t input_2_mult, const int32_t input_2_shift, const int32_t left_shift, int16_t *output, const int32_t out_offset, const int32_t out_mult, const int32_t out_shift, const int32_t out_activation_min, const int32_t out_activation_max, const int32_t block_size)</td></tr>
<tr class="memdesc:ga9e988362b8860a0108e62caf03e19b45"><td class="mdescLeft">&#160;</td><td class="mdescRight">s16 elementwise add of two vectors  <a href="group__BasicMath.html#ga9e988362b8860a0108e62caf03e19b45">More...</a><br/></td></tr>
<tr class="separator:ga9e988362b8860a0108e62caf03e19b45"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga345f342f6368a715ca3816c3cc3b7f70"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__BasicMath.html#ga345f342f6368a715ca3816c3cc3b7f70">arm_elementwise_mul_s8</a> (const int8_t *input_1_vect, const int8_t *input_2_vect, const int32_t input_1_offset, const int32_t input_2_offset, int8_t *output, const int32_t out_offset, const int32_t out_mult, const int32_t out_shift, const int32_t out_activation_min, const int32_t out_activation_max, const int32_t block_size)</td></tr>
<tr class="memdesc:ga345f342f6368a715ca3816c3cc3b7f70"><td class="mdescLeft">&#160;</td><td class="mdescRight">s8 elementwise multiplication  <a href="group__BasicMath.html#ga345f342f6368a715ca3816c3cc3b7f70">More...</a><br/></td></tr>
<tr class="separator:ga345f342f6368a715ca3816c3cc3b7f70"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gab38457329632897eb6efedf9dfcef1e4"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__BasicMath.html#gab38457329632897eb6efedf9dfcef1e4">arm_elementwise_mul_s16</a> (const int16_t *input_1_vect, const int16_t *input_2_vect, const int32_t input_1_offset, const int32_t input_2_offset, int16_t *output, const int32_t out_offset, const int32_t out_mult, const int32_t out_shift, const int32_t out_activation_min, const int32_t out_activation_max, const int32_t block_size)</td></tr>
<tr class="memdesc:gab38457329632897eb6efedf9dfcef1e4"><td class="mdescLeft">&#160;</td><td class="mdescRight">s16 elementwise multiplication  <a href="group__BasicMath.html#gab38457329632897eb6efedf9dfcef1e4">More...</a><br/></td></tr>
<tr class="separator:gab38457329632897eb6efedf9dfcef1e4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga638e803b4fe00426f401783a6255ca30"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Acti.html#ga638e803b4fe00426f401783a6255ca30">arm_relu_q7</a> (q7_t *data, uint16_t size)</td></tr>
<tr class="memdesc:ga638e803b4fe00426f401783a6255ca30"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q7 RELU function.  <a href="group__Acti.html#ga638e803b4fe00426f401783a6255ca30">More...</a><br/></td></tr>
<tr class="separator:ga638e803b4fe00426f401783a6255ca30"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga01a7a7c17f0cd544e29c4752daeecdc3"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Acti.html#ga01a7a7c17f0cd544e29c4752daeecdc3">arm_relu6_s8</a> (q7_t *data, uint16_t size)</td></tr>
<tr class="memdesc:ga01a7a7c17f0cd544e29c4752daeecdc3"><td class="mdescLeft">&#160;</td><td class="mdescRight">s8 ReLU6 function  <a href="group__Acti.html#ga01a7a7c17f0cd544e29c4752daeecdc3">More...</a><br/></td></tr>
<tr class="separator:ga01a7a7c17f0cd544e29c4752daeecdc3"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga53bcc00e54b802919bb3c89c143ee5ba"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Acti.html#ga53bcc00e54b802919bb3c89c143ee5ba">arm_relu_q15</a> (q15_t *data, uint16_t size)</td></tr>
<tr class="memdesc:ga53bcc00e54b802919bb3c89c143ee5ba"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q15 RELU function.  <a href="group__Acti.html#ga53bcc00e54b802919bb3c89c143ee5ba">More...</a><br/></td></tr>
<tr class="separator:ga53bcc00e54b802919bb3c89c143ee5ba"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga79f11131ae6767d60e03b1f6506b1af8"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Acti.html#ga79f11131ae6767d60e03b1f6506b1af8">arm_nn_activations_direct_q7</a> (q7_t *data, uint16_t size, uint16_t int_width, <a class="el" href="arm__nnfunctions_8h.html#a7f41aa78cd9a0552fae9b348ee4831a0">arm_nn_activation_type</a> type)</td></tr>
<tr class="memdesc:ga79f11131ae6767d60e03b1f6506b1af8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q7 neural network activation function using direct table look-up.  <a href="group__Acti.html#ga79f11131ae6767d60e03b1f6506b1af8">More...</a><br/></td></tr>
<tr class="separator:ga79f11131ae6767d60e03b1f6506b1af8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga8932b57c8d0ee757511af2d40dcc11e7"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Acti.html#ga8932b57c8d0ee757511af2d40dcc11e7">arm_nn_activations_direct_q15</a> (q15_t *data, uint16_t size, uint16_t int_width, <a class="el" href="arm__nnfunctions_8h.html#a7f41aa78cd9a0552fae9b348ee4831a0">arm_nn_activation_type</a> type)</td></tr>
<tr class="memdesc:ga8932b57c8d0ee757511af2d40dcc11e7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q15 neural network activation function using direct table look-up.  <a href="group__Acti.html#ga8932b57c8d0ee757511af2d40dcc11e7">More...</a><br/></td></tr>
<tr class="separator:ga8932b57c8d0ee757511af2d40dcc11e7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga99afcdcc61eaf429ab3ee823702e44ce"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Pooling.html#ga99afcdcc61eaf429ab3ee823702e44ce">arm_maxpool_q7_HWC</a> (q7_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const uint16_t dim_im_out, q7_t *bufferA, q7_t *Im_out)</td></tr>
<tr class="memdesc:ga99afcdcc61eaf429ab3ee823702e44ce"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q7 max pooling function.  <a href="group__Pooling.html#ga99afcdcc61eaf429ab3ee823702e44ce">More...</a><br/></td></tr>
<tr class="separator:ga99afcdcc61eaf429ab3ee823702e44ce"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gae4a7b07f97ec4313524c9fb9fbcb1f6a"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Pooling.html#gae4a7b07f97ec4313524c9fb9fbcb1f6a">arm_avepool_q7_HWC</a> (q7_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const uint16_t dim_im_out, q7_t *bufferA, q7_t *Im_out)</td></tr>
<tr class="memdesc:gae4a7b07f97ec4313524c9fb9fbcb1f6a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q7 average pooling function.  <a href="group__Pooling.html#gae4a7b07f97ec4313524c9fb9fbcb1f6a">More...</a><br/></td></tr>
<tr class="separator:gae4a7b07f97ec4313524c9fb9fbcb1f6a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gafa69cdb8a711ddef477d330204fa412c"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Pooling.html#gafa69cdb8a711ddef477d330204fa412c">arm_avgpool_s8</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__pool__params.html">cmsis_nn_pool_params</a> *pool_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:gafa69cdb8a711ddef477d330204fa412c"><td class="mdescLeft">&#160;</td><td class="mdescRight">s8 average pooling function.  <a href="group__Pooling.html#gafa69cdb8a711ddef477d330204fa412c">More...</a><br/></td></tr>
<tr class="separator:gafa69cdb8a711ddef477d330204fa412c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga08263590a94c39b4c1c199501ca166e4"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Pooling.html#ga08263590a94c39b4c1c199501ca166e4">arm_avgpool_s8_get_buffer_size</a> (const int dim_dst_width, const int ch_src)</td></tr>
<tr class="memdesc:ga08263590a94c39b4c1c199501ca166e4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the required buffer size for S8 average pooling function.  <a href="group__Pooling.html#ga08263590a94c39b4c1c199501ca166e4">More...</a><br/></td></tr>
<tr class="separator:ga08263590a94c39b4c1c199501ca166e4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac4d4234273f92a06a09228677a152d2c"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="arm__nnfunctions_8h.html#ac4d4234273f92a06a09228677a152d2c">arm_avgpool_s16</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__pool__params.html">cmsis_nn_pool_params</a> *pool_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const int16_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, int16_t *output_data)</td></tr>
<tr class="memdesc:ac4d4234273f92a06a09228677a152d2c"><td class="mdescLeft">&#160;</td><td class="mdescRight">s16 average pooling function.  <a href="#ac4d4234273f92a06a09228677a152d2c">More...</a><br/></td></tr>
<tr class="separator:ac4d4234273f92a06a09228677a152d2c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gac5f8a447fac56e1c48f353a746ec411a"><td class="memItemLeft" align="right" valign="top">int32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Pooling.html#gac5f8a447fac56e1c48f353a746ec411a">arm_avgpool_s16_get_buffer_size</a> (const int dim_dst_width, const int ch_src)</td></tr>
<tr class="memdesc:gac5f8a447fac56e1c48f353a746ec411a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the required buffer size for S16 average pooling function.  <a href="group__Pooling.html#gac5f8a447fac56e1c48f353a746ec411a">More...</a><br/></td></tr>
<tr class="separator:gac5f8a447fac56e1c48f353a746ec411a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga3628978b2ab0b43d8f8723118c12acc4"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Pooling.html#ga3628978b2ab0b43d8f8723118c12acc4">arm_max_pool_s8</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__pool__params.html">cmsis_nn_pool_params</a> *pool_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:ga3628978b2ab0b43d8f8723118c12acc4"><td class="mdescLeft">&#160;</td><td class="mdescRight">s8 max pooling function.  <a href="group__Pooling.html#ga3628978b2ab0b43d8f8723118c12acc4">More...</a><br/></td></tr>
<tr class="separator:ga3628978b2ab0b43d8f8723118c12acc4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga8bc3b3dd1fcf5ac462aa28bdddc0e3bb"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Pooling.html#ga8bc3b3dd1fcf5ac462aa28bdddc0e3bb">arm_max_pool_s16</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *ctx, const <a class="el" href="structcmsis__nn__pool__params.html">cmsis_nn_pool_params</a> *pool_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const int16_t *src, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *filter_dims, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, int16_t *dst)</td></tr>
<tr class="memdesc:ga8bc3b3dd1fcf5ac462aa28bdddc0e3bb"><td class="mdescLeft">&#160;</td><td class="mdescRight">s16 max pooling function.  <a href="group__Pooling.html#ga8bc3b3dd1fcf5ac462aa28bdddc0e3bb">More...</a><br/></td></tr>
<tr class="separator:ga8bc3b3dd1fcf5ac462aa28bdddc0e3bb"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga89aff212a97a3cf32d9d7ddf11a8f43e"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Softmax.html#ga89aff212a97a3cf32d9d7ddf11a8f43e">arm_softmax_q7</a> (const q7_t *vec_in, const uint16_t dim_vec, q7_t *p_out)</td></tr>
<tr class="memdesc:ga89aff212a97a3cf32d9d7ddf11a8f43e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q7 softmax function.  <a href="group__Softmax.html#ga89aff212a97a3cf32d9d7ddf11a8f43e">More...</a><br/></td></tr>
<tr class="separator:ga89aff212a97a3cf32d9d7ddf11a8f43e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga894cfd80c260b946702755b5754e520f"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Softmax.html#ga894cfd80c260b946702755b5754e520f">arm_softmax_with_batch_q7</a> (const q7_t *vec_in, const uint16_t nb_batches, const uint16_t dim_vec, q7_t *p_out)</td></tr>
<tr class="memdesc:ga894cfd80c260b946702755b5754e520f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q7 softmax function with batch parameter.  <a href="group__Softmax.html#ga894cfd80c260b946702755b5754e520f">More...</a><br/></td></tr>
<tr class="separator:ga894cfd80c260b946702755b5754e520f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga1cacd8b84b8363079311987d0016ebe5"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Softmax.html#ga1cacd8b84b8363079311987d0016ebe5">arm_softmax_q15</a> (const q15_t *vec_in, const uint16_t dim_vec, q15_t *p_out)</td></tr>
<tr class="memdesc:ga1cacd8b84b8363079311987d0016ebe5"><td class="mdescLeft">&#160;</td><td class="mdescRight">Q15 softmax function.  <a href="group__Softmax.html#ga1cacd8b84b8363079311987d0016ebe5">More...</a><br/></td></tr>
<tr class="separator:ga1cacd8b84b8363079311987d0016ebe5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gaf309cdd53978a85a39c9bfdc476aea17"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Softmax.html#gaf309cdd53978a85a39c9bfdc476aea17">arm_softmax_s8</a> (const int8_t *input, const int32_t num_rows, const int32_t row_size, const int32_t mult, const int32_t shift, const int32_t diff_min, int8_t *output)</td></tr>
<tr class="memdesc:gaf309cdd53978a85a39c9bfdc476aea17"><td class="mdescLeft">&#160;</td><td class="mdescRight">S8 softmax function.  <a href="group__Softmax.html#gaf309cdd53978a85a39c9bfdc476aea17">More...</a><br/></td></tr>
<tr class="separator:gaf309cdd53978a85a39c9bfdc476aea17"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga4c00979132b735e75525296bb5fa830f"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Softmax.html#ga4c00979132b735e75525296bb5fa830f">arm_softmax_s8_s16</a> (const int8_t *input, const int32_t num_rows, const int32_t row_size, const int32_t mult, const int32_t shift, const int32_t diff_min, int16_t *output)</td></tr>
<tr class="memdesc:ga4c00979132b735e75525296bb5fa830f"><td class="mdescLeft">&#160;</td><td class="mdescRight">S8 to s16 softmax function.  <a href="group__Softmax.html#ga4c00979132b735e75525296bb5fa830f">More...</a><br/></td></tr>
<tr class="separator:ga4c00979132b735e75525296bb5fa830f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga3bc3ad13727a8a9d2cf7d0fba1209879"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Softmax.html#ga3bc3ad13727a8a9d2cf7d0fba1209879">arm_softmax_s16</a> (const int16_t *input, const int32_t num_rows, const int32_t row_size, const int32_t mult, const int32_t shift, const <a class="el" href="structcmsis__nn__softmax__lut__s16.html">cmsis_nn_softmax_lut_s16</a> *softmax_params, int16_t *output)</td></tr>
<tr class="memdesc:ga3bc3ad13727a8a9d2cf7d0fba1209879"><td class="mdescLeft">&#160;</td><td class="mdescRight">S16 softmax function.  <a href="group__Softmax.html#ga3bc3ad13727a8a9d2cf7d0fba1209879">More...</a><br/></td></tr>
<tr class="separator:ga3bc3ad13727a8a9d2cf7d0fba1209879"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gaa1627ed96bd597a8046d00689f077dce"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Softmax.html#gaa1627ed96bd597a8046d00689f077dce">arm_softmax_u8</a> (const uint8_t *input, const int32_t num_rows, const int32_t row_size, const int32_t mult, const int32_t shift, const int32_t diff_min, uint8_t *output)</td></tr>
<tr class="memdesc:gaa1627ed96bd597a8046d00689f077dce"><td class="mdescLeft">&#160;</td><td class="mdescRight">U8 softmax function.  <a href="group__Softmax.html#gaa1627ed96bd597a8046d00689f077dce">More...</a><br/></td></tr>
<tr class="separator:gaa1627ed96bd597a8046d00689f077dce"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga064686bdb2a6e52110b302255dad6009"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__NNConv.html#ga064686bdb2a6e52110b302255dad6009">arm_depthwise_conv_u8_basic_ver1</a> (const uint8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_ch, const uint8_t *kernel, const uint16_t kernel_x, const uint16_t kernel_y, const int16_t ch_mult, const int16_t pad_x, const int16_t pad_y, const int16_t stride_x, const int16_t stride_y, const int16_t dilation_x, const int16_t dilation_y, const int32_t *bias, const int32_t input_offset, const int32_t filter_offset, const int32_t output_offset, uint8_t *output, const uint16_t output_x, const uint16_t output_y, const int32_t output_activation_min, const int32_t output_activation_max, const int32_t out_shift, const int32_t out_mult)</td></tr>
<tr class="memdesc:ga064686bdb2a6e52110b302255dad6009"><td class="mdescLeft">&#160;</td><td class="mdescRight">uint8 depthwise convolution function with asymmetric quantization Unless specified otherwise, arguments are mandatory.  <a href="group__NNConv.html#ga064686bdb2a6e52110b302255dad6009">More...</a><br/></td></tr>
<tr class="separator:ga064686bdb2a6e52110b302255dad6009"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga8cc1dfb7b2b083935a97dc4f24d0533c"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Reshape.html#ga8cc1dfb7b2b083935a97dc4f24d0533c">arm_reshape_s8</a> (const int8_t *input, int8_t *output, const uint32_t total_size)</td></tr>
<tr class="memdesc:ga8cc1dfb7b2b083935a97dc4f24d0533c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Reshape a s8 vector into another with different shape.  <a href="group__Reshape.html#ga8cc1dfb7b2b083935a97dc4f24d0533c">More...</a><br/></td></tr>
<tr class="separator:ga8cc1dfb7b2b083935a97dc4f24d0533c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gac06ac3c87cad1cfb14aa24b19124fcfd"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Concatenation.html#gac06ac3c87cad1cfb14aa24b19124fcfd">arm_concatenation_s8_x</a> (const int8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_z, const uint16_t input_w, int8_t *output, const uint16_t output_x, const uint32_t offset_x)</td></tr>
<tr class="memdesc:gac06ac3c87cad1cfb14aa24b19124fcfd"><td class="mdescLeft">&#160;</td><td class="mdescRight">int8/uint8 concatenation function to be used for concatenating N-tensors along the X axis This function should be called for each input tensor to concatenate. The argument offset_x will be used to store the input tensor in the correct position in the output tensor  <a href="group__Concatenation.html#gac06ac3c87cad1cfb14aa24b19124fcfd">More...</a><br/></td></tr>
<tr class="separator:gac06ac3c87cad1cfb14aa24b19124fcfd"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gaf0b76b039f66f34ec99503193a015ff6"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Concatenation.html#gaf0b76b039f66f34ec99503193a015ff6">arm_concatenation_s8_y</a> (const int8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_z, const uint16_t input_w, int8_t *output, const uint16_t output_y, const uint32_t offset_y)</td></tr>
<tr class="memdesc:gaf0b76b039f66f34ec99503193a015ff6"><td class="mdescLeft">&#160;</td><td class="mdescRight">int8/uint8 concatenation function to be used for concatenating N-tensors along the Y axis This function should be called for each input tensor to concatenate. The argument offset_y will be used to store the input tensor in the correct position in the output tensor  <a href="group__Concatenation.html#gaf0b76b039f66f34ec99503193a015ff6">More...</a><br/></td></tr>
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<tr class="memitem:ga9ae180a44e18ee58936dba1e0564560b"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Concatenation.html#ga9ae180a44e18ee58936dba1e0564560b">arm_concatenation_s8_z</a> (const int8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_z, const uint16_t input_w, int8_t *output, const uint16_t output_z, const uint32_t offset_z)</td></tr>
<tr class="memdesc:ga9ae180a44e18ee58936dba1e0564560b"><td class="mdescLeft">&#160;</td><td class="mdescRight">int8/uint8 concatenation function to be used for concatenating N-tensors along the Z axis This function should be called for each input tensor to concatenate. The argument offset_z will be used to store the input tensor in the correct position in the output tensor  <a href="group__Concatenation.html#ga9ae180a44e18ee58936dba1e0564560b">More...</a><br/></td></tr>
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<tr class="memitem:gaf2ec7d439726caa96e0b3dc989b34d64"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Concatenation.html#gaf2ec7d439726caa96e0b3dc989b34d64">arm_concatenation_s8_w</a> (const int8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_z, const uint16_t input_w, int8_t *output, const uint32_t offset_w)</td></tr>
<tr class="memdesc:gaf2ec7d439726caa96e0b3dc989b34d64"><td class="mdescLeft">&#160;</td><td class="mdescRight">int8/uint8 concatenation function to be used for concatenating N-tensors along the W axis (Batch size) This function should be called for each input tensor to concatenate. The argument offset_w will be used to store the input tensor in the correct position in the output tensor  <a href="group__Concatenation.html#gaf2ec7d439726caa96e0b3dc989b34d64">More...</a><br/></td></tr>
<tr class="separator:gaf2ec7d439726caa96e0b3dc989b34d64"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gafbf5ad3c931103dff943fc39f8f9d519"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__SVDF.html#gafbf5ad3c931103dff943fc39f8f9d519">arm_svdf_s8</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *input_ctx, const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *output_ctx, const <a class="el" href="structcmsis__nn__svdf__params.html">cmsis_nn_svdf_params</a> *svdf_params, const <a class="el" href="structcmsis__nn__per__tensor__quant__params.html">cmsis_nn_per_tensor_quant_params</a> *input_quant_params, const <a class="el" href="structcmsis__nn__per__tensor__quant__params.html">cmsis_nn_per_tensor_quant_params</a> *output_quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *state_dims, q7_t *state_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *weights_feature_dims, const q7_t *weights_feature_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *weights_time_dims, const q7_t *weights_time_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const q31_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:gafbf5ad3c931103dff943fc39f8f9d519"><td class="mdescLeft">&#160;</td><td class="mdescRight">s8 SVDF function with 8 bit state tensor and 8 bit time weights  <a href="group__SVDF.html#gafbf5ad3c931103dff943fc39f8f9d519">More...</a><br/></td></tr>
<tr class="separator:gafbf5ad3c931103dff943fc39f8f9d519"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga3dc1b94f56e80e5b80b353ce8d9650b3"><td class="memItemLeft" align="right" valign="top">arm_status&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__SVDF.html#ga3dc1b94f56e80e5b80b353ce8d9650b3">arm_svdf_state_s16_s8</a> (const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *input_ctx, const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *output_ctx, const <a class="el" href="structcmsis__nn__svdf__params.html">cmsis_nn_svdf_params</a> *svdf_params, const <a class="el" href="structcmsis__nn__per__tensor__quant__params.html">cmsis_nn_per_tensor_quant_params</a> *input_quant_params, const <a class="el" href="structcmsis__nn__per__tensor__quant__params.html">cmsis_nn_per_tensor_quant_params</a> *output_quant_params, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *input_dims, const q7_t *input_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *state_dims, q15_t *state_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *weights_feature_dims, const q7_t *weights_feature_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *weights_time_dims, const q15_t *weights_time_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *bias_dims, const q31_t *bias_data, const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *output_dims, q7_t *output_data)</td></tr>
<tr class="memdesc:ga3dc1b94f56e80e5b80b353ce8d9650b3"><td class="mdescLeft">&#160;</td><td class="mdescRight">s8 SVDF function with 16 bit state tensor and 16 bit time weights  <a href="group__SVDF.html#ga3dc1b94f56e80e5b80b353ce8d9650b3">More...</a><br/></td></tr>
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<h2 class="groupheader">Macro Definition Documentation</h2>
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          <td class="memname">#define USE_INTRINSIC</td>
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<h2 class="groupheader">Enumeration Type Documentation</h2>
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          <td class="memname">enum <a class="el" href="arm__nnfunctions_8h.html#a7f41aa78cd9a0552fae9b348ee4831a0">arm_nn_activation_type</a></td>
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<table class="fieldtable">
<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><em><a class="anchor" id="a7f41aa78cd9a0552fae9b348ee4831a0a49b307e029715fbaa6f3101c806b8c54"></a>ARM_SIGMOID</em>&#160;</td><td class="fielddoc">
<p>Sigmoid activation function </p>
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<tr><td class="fieldname"><em><a class="anchor" id="a7f41aa78cd9a0552fae9b348ee4831a0ac24e4db95c986f16c10dca71a4b4e1c5"></a>ARM_TANH</em>&#160;</td><td class="fielddoc">
<p>Tanh activation function </p>
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<h2 class="groupheader">Function Documentation</h2>
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          <td class="memname">arm_status arm_avgpool_s16 </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="structcmsis__nn__context.html">cmsis_nn_context</a> *&#160;</td>
          <td class="paramname"><em>ctx</em>, </td>
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          <td class="paramtype">const <a class="el" href="structcmsis__nn__pool__params.html">cmsis_nn_pool_params</a> *&#160;</td>
          <td class="paramname"><em>pool_params</em>, </td>
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          <td class="paramkey"></td>
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          <td class="paramtype">const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *&#160;</td>
          <td class="paramname"><em>input_dims</em>, </td>
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          <td class="paramtype">const int16_t *&#160;</td>
          <td class="paramname"><em>input_data</em>, </td>
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          <td class="paramtype">const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *&#160;</td>
          <td class="paramname"><em>filter_dims</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const <a class="el" href="structcmsis__nn__dims.html">cmsis_nn_dims</a> *&#160;</td>
          <td class="paramname"><em>output_dims</em>, </td>
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          <td class="paramkey"></td>
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          <td class="paramtype">int16_t *&#160;</td>
          <td class="paramname"><em>output_data</em>&#160;</td>
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          <td>)</td>
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<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in,out]</td><td class="paramname">ctx</td><td>Function context (e.g. temporary buffer). Check the function definition file to see if an additional buffer is required. Optional function {API}_get_buffer_size() provides the buffer size if an additional buffer is required. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">pool_params</td><td>Pooling parameters </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_dims</td><td>Input (activation) tensor dimensions. Format: [H, W, C_IN] Argument 'N' is not used. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_data</td><td>Input (activation) data pointer. Data type: int16 </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">filter_dims</td><td>Filter tensor dimensions. Format: [H, W] Argument N and C are not used. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">output_dims</td><td>Output tensor dimensions. Format: [H, W, C_OUT] Argument N is not used. C_OUT equals C_IN. </td></tr>
    <tr><td class="paramdir">[in,out]</td><td class="paramname">output_data</td><td>Output data pointer. Data type: int16 </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>The function returns <code>ARM_MATH_SUCCESS</code> - Successful operation</dd></dl>
<ul>
<li>Supported Framework: TensorFlow Lite </li>
</ul>

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          <td class="memname">q7_t* arm_nn_mat_mult_kernel_q7_q15 </td>
          <td>(</td>
          <td class="paramtype">const q7_t *&#160;</td>
          <td class="paramname"><em>pA</em>, </td>
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          <td class="paramkey"></td>
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          <td class="paramtype">const q15_t *&#160;</td>
          <td class="paramname"><em>pInBuffer</em>, </td>
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          <td class="paramkey"></td>
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          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>ch_im_out</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>numCol_A</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>bias_shift</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>out_shift</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const q7_t *&#160;</td>
          <td class="paramname"><em>bias</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">q7_t *&#160;</td>
          <td class="paramname"><em>pOut</em>&#160;</td>
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          <td>)</td>
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<p>These functions are used within convolution layer functions for matrix multiplication.</p>
<p>The implementation is similar to CMSIS-DSP arm_mat_mult functions with one Q7 and one Q15 operands. The Q15 operand is the im2col output which is always with 2 columns. Matrix-multiplication function for convolution </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">pA</td><td>pointer to operand A </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">pInBuffer</td><td>pointer to operand B, always conssists of 2 vectors </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">ch_im_out</td><td>numRow of A </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">numCol_A</td><td>numCol of A </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">bias_shift</td><td>amount of left-shift for bias </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">out_shift</td><td>amount of right-shift for output </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">bias</td><td>the bias </td></tr>
    <tr><td class="paramdir">[in,out]</td><td class="paramname">pOut</td><td>pointer to output </td></tr>
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  </dd>
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<dl class="section return"><dt>Returns</dt><dd>The function returns the incremented output pointer</dd></dl>
<p>Matrix-Multiplication Kernels for Convolution.</p>
<p>Refer to header file for details. </p>

<p>References <a class="el" href="arm__nnsupportfunctions_8h.html#afdda94a339b76615d3161e9fc63f4d21">arm_nn_read_q15x2_ia()</a>, and <a class="el" href="arm__nnsupportfunctions_8h.html#a4cbd428a2b4a4f6b2a6e4219520c7ce0">NN_ROUND</a>.</p>

<p>Referenced by <a class="el" href="group__NNConv.html#ga210ae8d8fc1d12ee15b41f1fa6947681">arm_convolve_HWC_q7_basic()</a>, <a class="el" href="group__NNConv.html#ga4501fa22c0836002aa47ccc313dce252">arm_convolve_HWC_q7_basic_nonsquare()</a>, and <a class="el" href="group__NNConv.html#ga98f2ead67d7cbdf558b0cd8a3b8fc148">arm_convolve_HWC_q7_RGB()</a>.</p>

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